Classification of human activities and detection of behavioral anomalies using thermopile sensor and machine learning

Impending increase in senior population in developed countries is expected to overwhelm the health care system carry a significant social and economic cost. Number of solutions, that leverage ambient intelligence, have been proposed to help aging in place. HomeEXCEPT is working on a non-intrusive solution using a temperature sensor. The research will help in identification and classification of human activities, including fall detection. It will also help in uncovering patterns in daily activities of an individual and in identification of deviations from the usual behavior. This will be attained using machine learning and data mining techniques. The effort will improve sensor’s capability and its applicability in providing a comprehensive aide to aging in place.

Faculty Supervisor:

Student:

Partner:

Home Except Inc

Discipline:

Computer science

Sector:

Finance, insurance and business

University:

Saint Mary's University

Program:

Accelerate